{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 比赛题目：学术论文分类挑战赛\n",
    "- 比赛链接：http://challenge.xfyun.cn/topic/info?type=academic-paper-classification\n",
    "- 比赛任务：本次赛题希望参赛选手利用论文信息：论文id、标题、摘要，划分论文具体类别。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 导入包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install simpletransformers -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "from simpletransformers.classification import ClassificationModel, ClassificationArgs\n",
    "from sklearn.metrics import accuracy_score,roc_auc_score,f1_score\n",
    "import gc\n",
    "import os\n",
    "import torch\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\" # 设置显卡\n",
    "# 配置\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina' # 主题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x293eb3d4e10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.set(style='whitegrid', palette='muted', font_scale=1.2)\n",
    "HAPPY_COLORS_PALETTE = [\"#01BEFE\", \"#FFDD00\", \"#FF7D00\", \"#FF006D\", \"#ADFF02\", \"#8F00FF\"]\n",
    "sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))\n",
    "rcParams['figure.figsize'] = 25, 20\n",
    "RANDOM_SEED = 42\n",
    "np.random.seed(RANDOM_SEED)\n",
    "torch.manual_seed(RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('data/train/train.csv', sep='\\t')\n",
    "test = pd.read_csv('data/test/test.csv', sep='\\t')\n",
    "sub = pd.read_csv('data/sample_submit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>paperid</th>\n",
       "      <th>title</th>\n",
       "      <th>abstract</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_00000</td>\n",
       "      <td>Hard but Robust, Easy but Sensitive: How Encod...</td>\n",
       "      <td>Neural machine translation (NMT) typically a...</td>\n",
       "      <td>cs.CL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_00001</td>\n",
       "      <td>An Easy-to-use Real-world Multi-objective Opti...</td>\n",
       "      <td>Although synthetic test problems are widely ...</td>\n",
       "      <td>cs.NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_00002</td>\n",
       "      <td>Exploration of reproducibility issues in scien...</td>\n",
       "      <td>This is the first part of a small-scale expl...</td>\n",
       "      <td>cs.DL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_00003</td>\n",
       "      <td>Scheduled Sampling for Transformers</td>\n",
       "      <td>Scheduled sampling is a technique for avoidi...</td>\n",
       "      <td>cs.CL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_00004</td>\n",
       "      <td>Hybrid Forests for Left Ventricle Segmentation...</td>\n",
       "      <td>Machine learning models produce state-of-the...</td>\n",
       "      <td>cs.CV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       paperid                                              title  \\\n",
       "0  train_00000  Hard but Robust, Easy but Sensitive: How Encod...   \n",
       "1  train_00001  An Easy-to-use Real-world Multi-objective Opti...   \n",
       "2  train_00002  Exploration of reproducibility issues in scien...   \n",
       "3  train_00003                Scheduled Sampling for Transformers   \n",
       "4  train_00004  Hybrid Forests for Left Ventricle Segmentation...   \n",
       "\n",
       "                                            abstract categories  \n",
       "0    Neural machine translation (NMT) typically a...      cs.CL  \n",
       "1    Although synthetic test problems are widely ...      cs.NE  \n",
       "2    This is the first part of a small-scale expl...      cs.DL  \n",
       "3    Scheduled sampling is a technique for avoidi...      cs.CL  \n",
       "4    Machine learning models produce state-of-the...      cs.CV  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000, 4), (10000, 3), (10000, 2))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据大小\n",
    "train.shape,test.shape,sub.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 查看缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "paperid       0\n",
       "title         0\n",
       "abstract      0\n",
       "categories    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看是否有缺失值\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "paperid     0\n",
       "title       0\n",
       "abstract    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   paperid     50000 non-null  object\n",
      " 1   title       50000 non-null  object\n",
      " 2   abstract    50000 non-null  object\n",
      " 3   categories  50000 non-null  object\n",
      "dtypes: object(4)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   paperid   10000 non-null  object\n",
      " 1   title     10000 non-null  object\n",
      " 2   abstract  10000 non-null  object\n",
      "dtypes: object(3)\n",
      "memory usage: 234.5+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train null nums\n",
      "paperid       0\n",
      "title         0\n",
      "abstract      0\n",
      "categories    0\n",
      "dtype: int64\n",
      "test null nums\n",
      "paperid     0\n",
      "title       0\n",
      "abstract    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"train null nums\")\n",
    "print(train.shape[0]-train.count())\n",
    "print(\"test null nums\")\n",
    "print(test.shape[0]-test.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 标签分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cs.CV    11038\n",
       "cs.CL     4260\n",
       "cs.NI     3218\n",
       "cs.CR     2798\n",
       "cs.AI     2706\n",
       "cs.DS     2509\n",
       "cs.DC     1994\n",
       "cs.SE     1940\n",
       "cs.RO     1884\n",
       "cs.LO     1741\n",
       "cs.LG     1352\n",
       "cs.SY     1292\n",
       "cs.CY     1228\n",
       "cs.DB      998\n",
       "cs.GT      984\n",
       "cs.HC      943\n",
       "cs.PL      841\n",
       "cs.IR      770\n",
       "cs.CC      719\n",
       "cs.NE      704\n",
       "cs.CG      683\n",
       "cs.OH      677\n",
       "cs.SI      603\n",
       "cs.DL      537\n",
       "cs.DM      523\n",
       "cs.FL      469\n",
       "cs.AR      363\n",
       "cs.CE      362\n",
       "cs.GR      314\n",
       "cs.MM      261\n",
       "cs.ET      230\n",
       "cs.MA      210\n",
       "cs.NA      176\n",
       "cs.SC      172\n",
       "cs.SD      140\n",
       "cs.PF      139\n",
       "cs.MS      105\n",
       "cs.OS       99\n",
       "cs.GL       18\n",
       "Name: categories, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['categories'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'categories count')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1141,
       "width": 1473
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train.categories)\n",
    "plt.xlabel('categories count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拼接title与abstract\n",
    "train['text']=train['title']+' '+train['abstract']\n",
    "test['text']=test['title']+' '+test['abstract']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 文本长度分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['text_len']=train['text'].map(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    50000.00000\n",
       "mean      1131.28478\n",
       "std        387.14365\n",
       "min         69.00000\n",
       "25%        860.00000\n",
       "50%       1117.00000\n",
       "75%       1393.00000\n",
       "max       3713.00000\n",
       "Name: text, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['text'].map(len).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10000.000000\n",
       "mean      1127.097700\n",
       "std        388.662603\n",
       "min         74.000000\n",
       "25%        855.750000\n",
       "50%       1111.000000\n",
       "75%       1385.250000\n",
       "max       3501.000000\n",
       "Name: text, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['text'].map(len).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1123,
       "width": 1477
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['text_len'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标签与唯一标签映射"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_id2cate=dict(enumerate(train.categories.unique()))\n",
    "label_cate2id={value: key for key,value in label_id2cate.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'cs.CL',\n",
       " 1: 'cs.NE',\n",
       " 2: 'cs.DL',\n",
       " 3: 'cs.CV',\n",
       " 4: 'cs.LG',\n",
       " 5: 'cs.DS',\n",
       " 6: 'cs.IR',\n",
       " 7: 'cs.RO',\n",
       " 8: 'cs.DM',\n",
       " 9: 'cs.CR',\n",
       " 10: 'cs.AR',\n",
       " 11: 'cs.NI',\n",
       " 12: 'cs.AI',\n",
       " 13: 'cs.SE',\n",
       " 14: 'cs.CG',\n",
       " 15: 'cs.LO',\n",
       " 16: 'cs.SY',\n",
       " 17: 'cs.GR',\n",
       " 18: 'cs.PL',\n",
       " 19: 'cs.SI',\n",
       " 20: 'cs.OH',\n",
       " 21: 'cs.HC',\n",
       " 22: 'cs.MA',\n",
       " 23: 'cs.GT',\n",
       " 24: 'cs.ET',\n",
       " 25: 'cs.FL',\n",
       " 26: 'cs.CC',\n",
       " 27: 'cs.DB',\n",
       " 28: 'cs.DC',\n",
       " 29: 'cs.CY',\n",
       " 30: 'cs.CE',\n",
       " 31: 'cs.MM',\n",
       " 32: 'cs.NA',\n",
       " 33: 'cs.PF',\n",
       " 34: 'cs.OS',\n",
       " 35: 'cs.SD',\n",
       " 36: 'cs.SC',\n",
       " 37: 'cs.MS',\n",
       " 38: 'cs.GL'}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_id2cate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cs.CL': 0,\n",
       " 'cs.NE': 1,\n",
       " 'cs.DL': 2,\n",
       " 'cs.CV': 3,\n",
       " 'cs.LG': 4,\n",
       " 'cs.DS': 5,\n",
       " 'cs.IR': 6,\n",
       " 'cs.RO': 7,\n",
       " 'cs.DM': 8,\n",
       " 'cs.CR': 9,\n",
       " 'cs.AR': 10,\n",
       " 'cs.NI': 11,\n",
       " 'cs.AI': 12,\n",
       " 'cs.SE': 13,\n",
       " 'cs.CG': 14,\n",
       " 'cs.LO': 15,\n",
       " 'cs.SY': 16,\n",
       " 'cs.GR': 17,\n",
       " 'cs.PL': 18,\n",
       " 'cs.SI': 19,\n",
       " 'cs.OH': 20,\n",
       " 'cs.HC': 21,\n",
       " 'cs.MA': 22,\n",
       " 'cs.GT': 23,\n",
       " 'cs.ET': 24,\n",
       " 'cs.FL': 25,\n",
       " 'cs.CC': 26,\n",
       " 'cs.DB': 27,\n",
       " 'cs.DC': 28,\n",
       " 'cs.CY': 29,\n",
       " 'cs.CE': 30,\n",
       " 'cs.MM': 31,\n",
       " 'cs.NA': 32,\n",
       " 'cs.PF': 33,\n",
       " 'cs.OS': 34,\n",
       " 'cs.SD': 35,\n",
       " 'cs.SC': 36,\n",
       " 'cs.MS': 37,\n",
       " 'cs.GL': 38}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_cate2id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['label']=train['categories'].map(label_cate2id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4  模型训练与评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train[['text','label']]\n",
    "train_y=train[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45000, 2), (5000, 2))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df=train[['text','label']][:45000]\n",
    "eval_df=train[['text','label']][45000:]\n",
    "train_df.shape,eval_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training roberta-base.........\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "478e16e8c9714cf7b9b5b4dd56abf9c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=45000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e02223a1a0404b95bfd60a2a7c98b559",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Epoch', max=1.0, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b6798c4d6904d3782b26494d8039f6e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 0 of 1', max=5625.0, style=ProgressStyle(de…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6c8673f71a7409996f2f052d9d79e9c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=5000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "540eaabf170e4a20b02fe2280aa88929",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Running Evaluation', max=625.0, style=ProgressStyle(descr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "{'mcc': 0.6061955662242928, 'acc': 0.6392, 'eval_loss': 1.335857630133629}\n"
     ]
    }
   ],
   "source": [
    "model_args = ClassificationArgs()\n",
    "model_args.max_seq_length = 512\n",
    "model_args.train_batch_size = 8\n",
    "model_args.num_train_epochs = 5\n",
    "model_args.fp16 = False\n",
    "model_args.evaluate_during_training = False\n",
    "model_args.overwrite_output_dir = True\n",
    "\n",
    "model_type = 'roberta'\n",
    "model_name = 'roberta-base'\n",
    "print(\"training {}.........\".format(model_name))\n",
    "model_args.cache_dir = './caches' + '/' + model_name.split('/')[-1]\n",
    "model_args.output_dir = './outputs' + '/' + model_name.split('/')[-1]\n",
    "\n",
    "model = ClassificationModel(\n",
    "    model_type,\n",
    "    model_name,\n",
    "    num_labels=39,\n",
    "    args=model_args)\n",
    "\n",
    "model.train_model(train_df, eval_df=eval_df)\n",
    "result, _, _ = model.eval_model(eval_df, acc=accuracy_score)\n",
    "print(result)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8a2fd8ba2fc49f9ae414e9d8ddbff1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=10000.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a16606f7162741c280d26aee90665250",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1250.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "69"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "for index, row in test.iterrows():\n",
    "    data.append(str(row['text']))\n",
    "predictions, raw_outputs = model.predict(data)\n",
    "sub = pd.read_csv('data/sample_submit.csv')\n",
    "sub['categories'] = predictions\n",
    "sub['categories'] = sub['categories'].map(label_id2cate)\n",
    "sub.to_csv('result/submit_{}.csv'.format(model_name), index=False)\n",
    "del model\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}